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Machine Learning Summit: Parameter Selection for ASTC Mapping Compression Using Machine Learning
As game hardware is constantly being updated, artists are producing mapping formats that range from 1k to 2k to 4k and even 8k. High-precision mapping makes the game world more realistic, but it also places a greater performance demand on the storage space, memory and GPU computing ability. To meet this challenge, new mapping compression formats are being proposed, with the most recent being the ASTC format. The ASTC format is currently being used on a large number of mobile devices due to its high compression ratio and low loss of accuracy. In this session, Wunong Li, Senior Programmer at Thunderfire Universe X Studio, will combine the algorithmic ideas of reinforcement learning with the real-life industrial scenario of mapping compression to show how this new solution can further improve the efficiency of mapping compression and balance the loss of accuracy. Moreover, he'll show everything you need to know about applying this technique to your project.
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